The present invention relates to a method for developing a process model for regulating a combustion process in a plant, in particular a power plant, a waste incineration plant or a plant for making cement, in which, while air is supplied, material is converted by way of the combustion process with at least one flame being formed, and the state of the system in the plant is described by state variables, with the method comprising setting up a neuronal network; then training the neuronal network using measurement data of the state variables, wherein the measurement data comprises measurement data from input channels and measurement data from at least one output channel; and then testing the neuronal network using further measurement data from the input channels and measurement data from the output channel, with the testing of the neuronal network comprising calculating predicted values for the output channel using the neuronal network and the further measurement data from the input channels, and calculating a standard deviation from deviations of the predicted values for the output channel from the measurement data of the output channel.
In a known method of the type described immediately above, in order to set up the neuronal network, the input channels are empirically selected and then retained in order to ensure a static topology. There is thus a risk that significant channels will not be considered, and also a risk that computing power will be used up for non-significant channels.